WPS4089
THE STRUCTURAL DETERMINANTS
OF EXTERNAL VULNERABILITY*
Norman V. Loayza Claudio Raddatz
World Bank World Bank
Abstract
This paper examines empirically how domestic structural characteristics related to
openness and product- and factor-market flexibility influence the impact that terms-of-
trade shocks can have on aggregate output. For this purpose, it applies an econometric
methodology based on semi-structural vector auto-regressions to a panel of 90 countries
with annual observations for the period 1974-2000. Using this methodology, the paper
isolates and standardizes the shocks, estimates their impact on GDP, and examines how
this impact depends on the domestic conditions outlined above. We find that larger trade
openness magnifies the output impact of external shocks, particularly the negative ones,
while improvements in labor market flexibility and financial openness reduce their
impact. Domestic financial depth has a more nuanced role in stabilizing the economy. It
helps reduce the impact of external shocks particularly in environments of high exposure
--that is, when trade and financial openness are high, firm entry is unrestricted, and labor
markets are rigid.
World Bank Policy Research Working Paper 4089, December 2006
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
* This paper was prepared for the conference "The Growth and Welfare Effects of
Macroeconomic Volatility," Barcelona, March 17-18, 2006. We thank Luis Servén, Paolo Mauro
(our discussant), Jim de Melo (the WBER editor), Romain Ranciere and Jaume Ventura
(conference organizers), as well as other conference participants for useful comments. We are
grateful to Koichi Kume for editorial assistance. Loayza: nloayza@worldbank.org. Raddatz:
craddatz@worldbank.org.
1
1. Introduction
Macroeconomic volatility is not only a source of business cycle uncertainty but
also a major cause of low economic growth. Ramey and Ramey (1995) were the first to
document this finding for a cross-section of countries. Fatás (2000) and Hnatkovska and
Loayza (2005) complemented it, showing that macroeconomic volatility is particularly
harmful for developing countries, where volatility is higher and its impact more
pronounced.
Among the causes of macroeconomic volatility, the incidence of external shocks
and in particular fluctuations in the terms of trade play an important role. Across
countries, about 10 percent of the variation in GDP growth and a quarter of the variation
in growth volatility can be explained by the observed differences in the volatility of
terms-of-trade changes (see Easterly et al. 1993 and Hnatkovska and Loayza 2005).
Terms-of-trade shocks have also been documented to have a significant impact on GDP
within countries (see Ahmed 2003, and Raddatz, 2005, among others), although on this
front the evidence of their relative importance vis-à-vis domestic shocks is more
controversial.1
Going beyond the average impact of external shocks, there is a rich literature that
suggests the possibility that the impact of an external shock on the real side of the
economy may be determined by domestic conditions that interact with the shocks to
produce macroeconomic stability or volatility as outcomes. The traditional approach to
analyze the domestic sources of vulnerability has stressed macroeconomic policy
responses in monetary, foreign exchange, and fiscal areas. A recent example of this
macroeconomic emphasis is Broda (2001), who compares the stabilization properties of
different exchange rate regimes in the face of terms-of-trade shocks. New developments
in the vulnerability literature have concentrated, instead, on the role of structural
characteristics related to the functioning of markets and institutions. Some studies stress
the role of factor and product market rigidities for the amplification of shocks at the
1On the one hand, Mendoza (1995), and Kose and Riezman (2001), using calibrated small-open economy
models find terms-of-trade shocks to account for almost half of economic fluctuations. On the other hand,
Hoffmaister, Roldós, and Wickham (1998), Ahmed (2003), and Raddatz (2005), among others, using time-
series analysis find that external shocks explain a much smaller fraction of output volatility (around 20
percent).
2
macroeconomic level. See, for example, Kiyotaki and Moore (1997), Bernanke and
Gertler (1989), Caballero and Hamour (1994, 1996, 1998), and Caballero and
Krishnamurty (2001). Others, such as Acemoglu et al. (2003), point to the relevance of
institutional development in the control of crises and management of shocks.
Notwithstanding these contributions, the extent to which domestic structural
characteristics can account for the relative instability of aggregate output remains an open
question. This paper contributes to this literature by examining how certain domestic
structural characteristics influence the impact that external shocks may have on aggregate
output. The broad issue under study here is whether a country's vulnerability to shocks is
not purely random but linked to structural characteristics driven at least partially by
policy and related specifically to trade and financial openness, financial depth, and labor
and firm flexibility.
For analytical purposes we can distinguish two aspects of output vulnerability to
external shocks. The first is the frequency and strength of the shocks affecting the
country. The second one is the effect that a shock of a given size and frequency can have
on the country's output. This paper is dedicated to the second aspect of vulnerability.
For this purpose and working with a panel sample of 90 countries and annual
observations for the period 1974-2000, the paper applies an econometric methodology
that isolates and standardizes the shocks, estimates their impact on GDP, and examines
how this impact depends on the domestic conditions outlined above.2
From an empirical perspective, the relevant question for the paper is whether the
differential impact of a given external shock is related to country characteristics.
Controlling for the size of the shocks is not an easy task. Most of the recent literature on
this issue has relied on either indirect evidence from difference-in-difference estimation
(see, for example, Braun and Larrain 2005, Caballero, Cowan, Engel, and Micco 2005,
and Raddatz 2005) or calibrated macroeconomic models developed to match certain
moments of developing countries' economic performance (for a survey, see Arellano and
Mendoza 2004). This paper takes a different approach and directly estimates the output
2An analogy can illustrate these ideas: how vulnerable people are to disease depends on the seriousness of
the disease itself (first aspect) and how well prepared they are to bear a given disease (second aspect). In
order to analyze the second aspect, it would be necessary to examine how people facing the same disease
(in type and strength) react. This would shed light on why some people suffer so much from an attack of,
say, the flu, while others remain unscathed.
3
impact of external shocks using semi-structural vector auto-regression analysis (VAR), as
applied to a (cross-country, time-series) panel data of aggregate variables. This
methodology requires the identification assumption that the relevant external variable,
that is, the change in the terms of trade, does not respond to either domestic output
changes or the variables that account for the country's structural characteristics. In
practice, this rather uncontroversial assumption amounts to a small-open-economy
condition for the countries included in the analysis. Similar applications of this
methodology can be found in Broda (2004), Ahmed (2002), Uribe and Yue (2003), and
Raddatz (2005). Controlling for the size of the shock, the analysis proceeds to account
for its interaction with the set of country characteristics under analysis and estimate its
conditional output impact.
The rest of the paper carries on as follows. Section 2 presents the econometric
methodology in detail and conducts the corresponding specification tests. Section 3
introduces the data, providing information on variable definitions and sources, as well as
the sample of countries and years under analysis. Section 4 presents the empirical results,
including the discussion of symmetric and asymmetric (positive/negative) effects, the
potential complementarity between structural characteristics, and a set of robustness
checks. Section 5 concludes.
2. Methodology
We estimate the impact of exogenous shocks on a country's economic
performance and its relation to a country's structural characteristics using a panel vector
auto-regression (panel VAR). In order to impose the minimum identification assumptions
possible (see below), we focus exclusively on terms-of-trade shocks. Therefore, for a
given country i, our semi-structural model corresponds to:
q
(1) Ai x = x
,0 i,t A i, j i,t- j+it
j=1
where xi = (tti ,yi )', is a vector that contains the first difference of the (log) terms-of-
,t ,t ,t
trade index ( tti ) and the log of real GDP per capita ( yi ), both as deviations from
,t ,t
4
their country-specific means.3 The matrices Ai contain the structural coefficients for the
, j
different lags included in the model (including the contemporaneous). The structural
errors i are i.i.d. with zero mean and a diagonal variance-covariance matrix .
,t
The identification assumption used in the paper is that, for a given country, terms-
of-trade changes are strictly exogenous. That is, we assume that tti do not respond to
,t
yi at any lags. This assumption is equivalent to imposing the following triangular
,t
structure in all the A matrices:
, j
Ai = ai 11 .
, j ai , j , j
21 ai0 22
For the developing and small developed countries included in this study, this
assumption should be uncontroversial. In fact, for the sample of countries included in this
study, a standard Granger causality cannot reject the hypothesis that output fluctuations
do not Granger-cause terms of trade fluctuations.4 The relatively weak assumption
required to identify the impact of a terms-of-trade shock is the reason why we focus
exclusively on these shocks. We believe that it is preferable to focus on a reduced set of
shocks that we can clearly identify that in a broader set of shocks that would require
strong and controversial identification assumptions. This means, however, that we have
to interpret our results with caution. Our statements on the role of different structural
characteristics on the amplification or dampening of shocks apply directly to terms-of-
trade shocks, and only indirectly to other exogenous contingencies that are correlated
with these shocks.5 It may well be the case, however, that some of the structural
characteristics will be especially important for other types of shocks that are not or
loosely correlated to those to the terms-of-trade.
Our baseline model corresponds to a panel VAR in which we assume that part of
the coefficients in the A matrices are common across cross-sectional units. As we are
3Of course, this is equivalent to including a country fixed effect in the VAR.
4When performed on a country-by-country basis, the test cannot reject the null in 77 of the 90 cases. In the
robustness section we will show that the results are not materially affected by excluding the 13 countries in
which the hypothesis that terms of trade are no Granger-caused by output is rejected.
5As we do not have a full structural model that accounts for all exogenous sources of fluctuations, ours is a
semi-structural model, and our terms of trade variable captures all strictly exogenous variables that are
correlated with the fluctuations in terms of trade.
5
interested in testing how different structural characteristics of a country affect the impact
of terms-of-trade shocks on output, which is captured on the ai , j
21coefficients, we permit
these coefficients to vary across countries according to the specific characteristics whose
role we want to determine. In particular, we assume that
ai, j
21= 0 + 1 ×OPENi + 2 ×FDEVi + 3 ×CAOPENi + 4×LABORi + 5×ENTRYi
j j j j
where OPENi , FDEVi , CAOPENi , LABORi , and ENTRYi are measures of trade
openness, financial development, capital account openness, labor flexibility, and firm
entry flexibility for country i that we describe below in section 3. In part of our analysis
we will also allow for the possibility that the role of these characteristics on the
transmission of terms-of-trade shocks may be different when there is a decrease in (log)
terms of trade (with respect to the mean change). In terms of the notation above, this
corresponds to allowing the coefficients to vary with the state of the terms-of-trade in
j
the following way:
= + if tti > 0
,t
- otherwise
where = ( 0,K, ,K, q), = (0 ,K,5 ), and + and
j j j j - are similarly
defined. The rest of the coefficients that capture the dynamics of the terms of trade and
the lagged effect of output on itself (the ai , j
11and ai, j
22coefficients respectively) are
restricted to be the same for all countries.
The use of panel VARs, with the consequent restrictions on the parameters, is
common in the recent literature that estimates the impact of exogenous shocks on
different macroeconomic variables (see Broda, 2004; Ahmed, 2003; Uribe and Yue,
2003) because the length of the time series dimension of the data (around 25 annual
observations) makes it difficult to estimate country specific dynamics. Using a panel
VAR approach instead increases the degrees of freedom of the estimation, and, if the
restrictions are correct, provides more efficient estimators. Of course, the obvious
disadvantage is that if the restrictions are incorrect the model may be incorrectly
specified.
6
A particular concern with this approach is that, as noticed by Pesaran and Smith
(1995), the assumption of common coefficients may lead to obtaining parameters that
underestimate (overestimate) the short (long) run impact of exogenous variables if the
dynamics differ importantly across countries. However, as demonstrated by Pakes and
Griliches (1984), if differences in slope coefficients are uncorrelated with the exogenous
variables the estimated parameters would be consistent estimators of the average
coefficients. This is an important result for our case, as we do not see a good reason to
believe that the effect of terms of trade in a country should be determined by the level of
terms of trade. Nevertheless, with the caveat of the lack of precision of the estimates, we
will also estimate the VAR on a country-by-country basis, without imposing any
restriction on the dynamics, and then relate the estimated country specific effects of
shocks to the structural characteristics we are studying. The results will prove to be very
similar to those obtained with our panel methodology.
As mentioned above, the variables in the VAR are the first differences of the (log)
terms of trade and output per capita. That is, we model the relevant series as difference-
stationary. There are two reasons for this modeling choice. First, standard tests tend to
suggest the presence of a unit root in the series. The results of those tests are summarized
in Table 1. Columns 1 and 2 show the results of the ADF tests performed on a country-
by-country basis for the cases where the underlying Dickey-Fuller tests have been
augmented with a number of lags that varies or is kept constant across countries
respectively.6 It is clear that in most cases the test cannot reject the null of a unit-root for
both series (about 85 percent of the time for both series when the median number of lags
is used for all countries). The panel based unit-root test suggested by Levin, Lin, and Chu
(2002), augmented by the median number of lags across countries (2 lags), reported in
column (3) provides a similar conclusion. The null of a unit root cannot be rejected.
Second, previous empirical papers in this literature (e.g. Ahmed, 2003, Broda, 2004) have
estimated difference stationary models, so this specification has the advantage of being
more directly comparable with the existing results.7
6 The number of lags for each country was determined using Hall's (1990) methodology. The common
number of lags used in column (2) corresponds to the median across countries (2 lags).
7Pedroni (1999) panel cointegration test, not reported, does not reject the null of no cointegration between
(log) terms of trade and output. The different statistics derived by Pedroni (1999) tend to give different
7
Regarding the number of lags, we use two annual lags in the benchmark
specification. This number of lags was obtained from standard lag selection tests (Akaike
information criterion, Schwartz information criterion, and Hannan-Quinn criterion).
Under the identification assumptions described above, we estimate the parameters
of the model using a two-step procedure in which we first estimate the reduced form
coefficients by OLS equation-by-equation and recover the impulse-response functions
(IRF) to each of the structural shocks using the reduced form coefficients and the
variance-covariance matrices of reduced form errors derived from these coefficients. The
confidence bands for the IRF were estimated by parametric bootstrapping assuming
normally distributed reduced form errors.8
3. Data
The main variables used in the paper are the following. Real GDP per-capita
corresponds to the GDP per capita in constant 2000 U.S. dollars and was obtained from
the WDI. The reason to use this series instead of the PPP adjusted ones, despite the
reduced international comparability, is that it has more recent coverage than the measures
from the Penn World Tables and longer coverage than the PPP series produced by the
World Bank. The terms-of-trade index is the ratio of export prices to import prices
computed using the current and constant price values of exports and imports from the
national accounts component of the Penn World Tables (version 6.1) and updated using
the terms-of-trade data from WDI. To reduce concerns about structural breaks we focus
on the post Bretton-Woods period, 1974-2000.
results but most of them cannot reject the null of no cointegration. As the power and size trade-off of the
different tests varies with the cross-sectional and time-series dimension of the panel (see Pedroni, 2004),
we focused on the statistics with that have the largest size (so tend to over-reject) and highest power at
short time dimensions. Those tests, corresponding to the panel and group t-statistics derive by Pedroni
(1999) clearly do not reject the null of no cointegration.
8The procedure can be briefly described as follows: (i) we use the estimated variance-covariance matrix of
the reduced form errors to simulate a random realization of the perturbations; (ii) we use the initial values
of the different variables, the baseline coefficients, and the simulated perturbations to simulate a new set of
observations for the variables in the VAR; (iii) we use these simulated observations to estimate a new set of
coefficients; (iv) we repeat this exercise 500 times; (v) we compute the IRF for each set of coefficients
obtained from the bootstrapping; (vi) we build a 90% confidence interval for the IRF by taking the 5th and
95th percentile of the empirical distribution of the IRF on a point-by-point basis.
8
The structural characteristics of the countries are captured in the following
variables. Trade openness is measured as the (log) of the ratio of total trade to GDP.
Financial development corresponds to the (log) of the ratio of the private credit provided
by banks and other financial institutions to GDP, obtained from Beck, Demirguc-Kunt,
and Levine (2000). When not available we used data on the domestic credit to private
sector (as a fraction of GDP) from WDI. Openness in capital account transactions is
captured by the Ito and Chinn (2002) index.9 The index is such that a higher value
indicates a higher degree of openness. The index of labor market flexibility is calculated
from data in World Bank (2003) and is a weighted average of three indicators --flexibility
of hiring, conditions of employment, and flexibility of firing-- as in Botero et al. (2004).
For this paper, the original index was rescaled to range between 0 and 1, with higher
values indicating more flexible labor markets. Finally, the index measuring the ease of
firm entry was calculated from data in World Bank (2003) and O'Driscoll, Feulner, and
O'Grady (2003) and is a weighted average of four indicators ­registration procedures,
cost of registration, days to registration, and burden entry regulations--as in Chang,
Kaltani, and Loayza (2005). This index also ranges from 0 to 1, with higher values
indicating less restricted firm entry.
The sample of countries used in the empirical analysis is shown in Table 2. The
sample includes 90 countries from different regions and income levels. The largest region
in the sample is Sub-Saharan Africa with 31 countries, followed by Latin America with
20, East Asia and Pacific and with 11, Middle East and North Africa and Western Europe
with 10 each, and South Asia, East and Central Europe, and North America with 4, 3, and
1 countries respectively. With respect to income, there are 36 low-income countries, 36
middle-income countries, and 18 high-income countries. The sample includes all
countries where at least 15 continuous observations of both terms-of-trade and output per
capita were available during the period 1974-2000 and with available measures of the
structural characteristics described above. We excluded from the sample large industrial
9 The Ito-Chin index corresponds to the first principal components of the following 4 binary variables
reported in the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER):
existence of multiple exchange rates, restrictions on current account, capital account transactions, and the
existence of requirements to surrender exports proceedings.
9
countries because of the possible endogeneity of their terms of trade,10 and five
developing countries where the terms of trade data exhibited long flat periods.11
The different columns of Table 2 show some summary statistics for each of these
variables for all countries in the sample. The cross-sectional correlations among these
variables are reported in Table 3. There we observe the well documented positive
correlations between the different structural characteristics and output growth, and
between the measures of volatility and growth. We also observe that all the structural
characteristics are positively correlated, although the magnitudes of the correlations are
not particularly large with the exception of the correlation between financial development
and firm flexibility, which reaches 66 percent. These relatively low correlations give us
ground to expect to be able to sort out the role of the different characteristics for the
transmission of shocks.
4. Results
Our basic results are derived from estimating the cumulative output effect of a
one-standard-deviation shock to the terms of trade at different levels of a particular
structural characteristic. As explained in the methodological section, we conduct this
estimation in the context of a panel (cross-country, time-series) vector autoregression
with (detrended log) GDP changes as the dependent variable and (detrended log) terms-
of-trade changes as the exogenous variable. We allow the output effect of terms-of-trade
shocks to vary with five country structural characteristics: trade openness, financial
depth, financial (or capital-account) openness, labor market flexibility, and ease of firm
entry. To analyze the effect of these structural factors, we compare the shock's
cumulative output impact measured at the 25th and the 75th percentiles of the world
distribution of each structural characteristic. Comparing the impact statistics at these
(relatively) low and high levels for a given structural factor provides a sense for how
much it contributes to amplifying or dampening the external shock.
10The excluded countries are United States, United Kingdom, France, Italy, Germany, and Japan.
11These countries are Cape Verde, Grenada, St. Lucia, St. Kitts and Nevis, and Nepal.
10
Figure 1 and Table 4 present the cumulative effect of a one-standard-deviation
shock in the terms of trade on the level of GDP per capita for low and high levels of each
country characteristic. In order to indicate the accuracy of the estimated impacts, Figure
1 also presents their 90% confidence bands, and Table 4 the corresponding (empirical)
standard errors.12 In order to have a benchmark for quantitative comparison, we
estimated the average cumulative output impact of a one-standard-deviation terms-of-
trade shock (that is, the impact calculated at the means of all structural characteristics)
and its value is approximately 1 percentage point of GDP.
The most noticeable result is that larger trade openness appears to increase the
cumulative output impact of terms-of-trade shocks. This is likely to be a size effect, as a
higher volume of trade implies a larger share of economic activities that trade prices can
influence. This effect is large: the output impact of the shock at the third quartile of trade
openness is 1.4 percentage points higher than at the first quartile. Conversely, higher
financial depth seems to have no effect on the impact of terms-of-trade shocks. This is
rather surprising given that financial depth is usually considered as an antidote to external
vulnerability. This is an important issue, and we'll revisit it at several points in this
paper.
An increase in financial openness does appear to reduce the effect of a terms-of-
trade shock, although by only a moderate margin: the difference in the cumulative output
impact between the 25th and 75th percentiles of financial openness is 0.34 percentage
points. The fact that access to international financial markets has a stabilizing effect
while domestic financial depth does not is puzzling. Below we examine a possible
interaction between these two structural characteristics. Easing firm entry has a small
amplifying effect of terms-of-trade shocks, but it fails to be statistically significant. The
entry of new firms may be compensated by exit in relatively equal amounts, and,
moreover, this process of firm dynamics may have different characteristics under
negative or positive shocks. For this and the other structural characteristics, we analyze
the possibility of asymmetric effects below.
12 Critical values and corresponding confidence intervals are obtained from the empirical distribution
derived through the parametric bootstrapping procedure described above.
11
Finally, of all structural characteristics considered here, improvement in labor
market flexibility has the strongest effect of reducing the impact of terms-of-trade shocks
on per capita GDP. The difference in the shock's cumulative output impact between the
first and third quartiles of labor market flexibility is 0.74 percentage points. The ability
of firms to adjust their activities on the labor margin seems crucial for the economy to
accommodate the shock.
Robustness
We examine the robustness of the basic results to the application of a longer lag
structure in the estimated VARs, changes in the sample of countries, the use of alternative
measures of financial depth, the inclusion of the exchange-rate regime as an additional
country characteristic, and the implementation of an alternative method to estimate the
effects of structural characteristics. The results are presented in Tables 5 and 6.
The benchmark results are obtained from panel VARs that include both
developing and developed countries and set a common lag structure of 2 lags. The first
two robustness checks refer to these features of estimation. First, to dispel concerns on
whether pre-estimation diagnostics could have indicated a longer lag structure, we re-
estimate the shock impacts from VARs with 3 lags for all countries. The results change
little if anything: there seems to be some reshuffling in the relative importance of the
structural characteristics, but the differences are quantitatively small and unlikely to be
statistically significant. A richer lag structure comes at the price of using fewer
observations for estimation, particularly in countries where the time series is rather
limited, and we prefer to continue working with VARs of 2 lags.
Second, in order to discard the possibility that our results are derived only from
the contrast between developing and developed countries, we exclude all OECD
countries from our sample, re-estimate the model, and compute again the impact
statistics. These results are qualitatively the same and quantitatively quite similar as
those obtained using the full sample. This similarity is noteworthy and indicates that our
results can be compared to those of studies that focus only on developing countries.13
13The results are also unaffected by the exclusion of the 13 countries where were we rejected the
hypothesis that output fluctuations Granger caused terms of trade fluctuations.
12
The absence of any clear effect of increasing financial depth on the impact of
external shocks is arguably the most surprising result of our basic exercises. Here we
examine whether this result is robust to changes in the measurement of financial depth.
In particular, it can be argued that larger financial depth appears not to reduce the impact
of shocks because it expands as these shocks occur (this is a variation of the reverse
causality argument). As in the empirical growth literature, we address this possibility by
using the initial measure of private credit/GDP instead of its period average as the proxy
for financial depth. However, the result on the effect of financial depth is basically
unchanged and, therefore, the puzzling irrelevance of financial depth continues.
The exchange-rate regime is usually considered a macroeconomic policy and not
a structural characteristic. That's why it was not included in the basic set of interactions.
However, since it has received so much attention in the stabilization literature and could
in principle be related to the structural characteristics considered here, we conduct an
additional exercise that includes the exchange rate regime as an additional interaction
variable. We follow the Gosh et al. (2000) classification to separate country-year
observations with a pegged regime from those with intermediate and floating regimes.
This exercise renders very similar results to those of the benchmark: Trade openness is
found to amplify the shocks, financial openness and labor market flexibility to mitigate
them, and financial depth and ease of firm entry to be negligible in this respect. The
effect of the exchange rate regime itself is quite small and statistically insignificant. This
result is, however, only tentative. The analysis of the exchange rate regime requires a
treatment of measurement issues that is out of the scope of this paper.
As explained in the methodological section, an alternative to estimating the
interactions model using panel data consists of estimating the simple model (with no
interactions) country-by-country, and then running a cross-country regression of the
resulting cumulative impacts on the five structural variables. The advantage of this
method is that it allows for full country heterogeneity in parameter estimation; this,
however, comes at the price of lower estimation efficiency and increased noise in the
individual country impulse responses.
Table 6 presents the results of this regression, estimated with a procedure that is
robust to the undue influence of outlying observations. The results are qualitatively
13
similar to those obtained from panel VARs. That is, the two most important country
characteristics that affect the shocks' impact are trade openness and labor market
flexibility, the former magnifying the impact and the latter reducing it. Financial depth
and ease of firm entry carry positive coefficients, but not large enough to be statistically
insignificant. Financial openness carries a negative coefficient, implying a stabilizing
effect, and this effect is close to achieving statistical significance.
Asymmetric effects
The analysis above allows us to determine if structural characteristics have a
stabilizing (or destabilizing) effect for all shocks, whether positive or negative. In
principle, however, this symmetric treatment could mask important differences on the
effects of structural characteristics for positive and negative shocks. For instance, an
ideal structural characteristic --one that in reality magnifies positive shocks and reduces
negative ones-- could be found to be ineffectual under a symmetric analysis. We now
consider separately the output response to negative and positive terms-of-trade shocks.
The results of the asymmetric analysis are presented in Table 7 and the
corresponding panels of Figures 2 and 3. The first thing to notice is that only increases in
trade openness and in labor market flexibility and only in the case of negative shocks
produce statistically significant results. The estimation of asymmetric shocks presents
larger standard errors as it uses fewer observations and suffers from wide data variations
associated to sign transitions. While the following discussion takes the point estimates at
face value, we acknowledge that small effects (those below 0.3 percentage points, as a
simple rule of thumb) are likely to be statistically irrelevant.
There is some evidence of asymmetric effects. The asymmetry in the effect of
trade openness is not present in the direction of the impact but in its magnitude. Larger
trade openness increases the (absolute) impact of both negative and positive shocks on
per capita GDP. However, its effect on the impact of negative shocks is larger than that
on positive ones. In fact, comparing the effects at the first and third quartiles of trade
openness, its negative effect is about four times as large as the positive one.
Higher financial depth has no significant effect on the impact of either positive or
negative terms-of-trade shocks. Therefore, its lack of relevance as shock stabilizer
14
cannot be explained by asymmetric effects. An increase in financial openness reduces
the (absolute) impact of both negative and positive shocks and by similar magnitudes
than under the case of symmetric effects (0.3-0.4 percentage points). It is not surprising,
then, that assuming symmetry in the case of financial openness produces more efficient
estimates and, thus, significant effects.
An improvement in labor market flexibility dampens the effect of both negative
and positive shocks, with a bit stronger effect on negative ones. Raising labor market
flexibility from the 25th to the 75th percentile of its world distribution helps reduce the
impact of negative shocks by 0.82 percentage points while reducing that of positive
shocks by 0.51 percentage points. Labor market flexibility is, thus, particularly important
in the face of adverse shocks. Finally, ease of firm entry also shows some evidence of an
asymmetric effect: whereas easing firm entry does not alter the impact of negative
shocks, it increases somewhat the consequences of positive ones. Improving the ease of
firm entry from the first to the third quartile of its world distribution increases the
cumulative output impact of positive shocks by 0.55 percentage points.
In summary, the following taxonomy arises from the analysis on asymmetric
effects. Trade openness amplifies both negative and positive shocks, ease of firm entry
magnifies only positive shocks, financial openness and labor market flexibility (most
clearly) help stabilize the economy after positive and negative shocks, and financial depth
appears to be inconsequential for the effect of either type of shock. However, as
mentioned above, estimation under asymmetric effects is rather imprecise and produces
statistically significant results only for larger trade openness (as shock magnifier) and
further flexibility in labor markets (as stabilizer).
The Role of Financial Depth: Complementarities
According to the evidence presented above, financial depth appears to have no
effect on the output impact of terms-of-trade shocks. This result is based on a model that
controls for other structural factors but does not condition the effect of financial depth
itself on these variables. The latter calls for a model that interacts financial depth with
other structural characteristics, thus allowing for complementarities between them. This
is necessary to assess whether financial depth becomes relevant under special
15
circumstances; for instance, in countries with a large degree of financial openness or low
labor market flexibility. Allowing for multiplicative interactions is complex enough, so
that we restrict this analysis to the case of symmetric effects (of positive and negative
shocks).
The summary results of the interactions model are presented in Table 8. In
particular, it shows the effect of improving financial depth from the 25th percentile
("Low") to the 75th percentile ("High") when each of the other characteristics are at their
respective 25th and 75th percentiles. In contrast to the basic one, the interactions model
indicates a relevant though nuanced role for financial depth in affecting the impact of
external shocks. At low trade openness, increasing financial depth raises the shock
impact; while at high trade openness, it reduces the impact. In both cases the effect of
larger financial depth is statistically significant and by around 0.55 percentage points in
magnitude.
Likewise, at low financial openness, increasing financial depth increases the
impact of the shock; while at high financial openness, the opposite occurs. The former
effect is small and not quite significant. However, at high levels of capital account
openness, the effect of improving financial depth is not only statistically significant but
also remarkably large (1.6 percentage points). This result is consistent with the literature
that emphasizes the complementarity between reforms in domestic and international
financial markets (see Caballero and Krishnamurthy, 2001, or Edwards, 2001, among
others).
An alternative reading of the previous results may help clarify the positive role of
financial development. First, although trade openness always increases the impact of a
shock, this is considerably smaller when the expansion in openness occurs in a country
with well developed local financial markets. Similarly, our findings indicate that higher
financial openness in an environment of underdeveloped local financial markets may
result in an increase in the impact of external shocks. In contrast, when financial
openness occurs in a country with well developed financial markets the impact of the
shocks is reduced.
The interaction between financial depth and the ease of firm entry is equally
noteworthy: improving financial depth when firm entry is unrestricted renders a large
16
payoff in terms of reducing the shock impact (by 1.5 percentage points). At low levels of
firm entry flexibility, improving financial depth also contributes significantly to reduce
the impact of shock but by less than half of the effect under flexible firm entry.
Whereas for trade openness, financial integration, and firm entry, there emerges a
pattern of complementarity with financial depth; the case of labor market flexibility
appears to be one of substitution. Improving financial depth has an attenuating effect on
the shock's impact when labor markets are rather rigid (by a significant 0.62 percentage
points). Improving financial depth has no effect when labor markets are flexible.
5. Concluding Remarks
What underlies a country's vulnerability to external shocks? Why do some
countries suffer so much in the face of terms-of-trade shocks while others remain
unscathed? With these questions in mind, in this paper we examine how certain domestic
characteristics influence the impact that terms-of-trade shocks can have on aggregate
output. The paper has an empirical objective but is motivated by the recent literature that
emphasizes the role of product- and factor-market rigidities as the source of
macroeconomic vulnerability.
The paper applies an econometric methodology based on semi-structural vector
auto-regressions to a panel of 90 countries with annual observations for the period 1974-
2000. Using this methodology, the paper identifies and standardizes the terms-of-trade
shocks affecting these countries, estimates their impact on (detrended) GDP per capita,
and examines how this impact depends on the countries' trade and financial openness,
domestic financial depth, and labor and firm-entry flexibility.
Our basic results indicate that the two most important country characteristics
affecting the shocks' output impact are trade openness and labor market flexibility, the
former magnifying the impact and the latter reducing it. Financial openness also shows a
significant stabilizing effect, but weaker in magnitude. Financial depth and ease of firm
entry do not seem to affect the shocks' impact significantly in the basic specification.
These results are robust to using a longer lag structure for the VARs, concentrating
exclusively on developing countries, substituting initial for average financial depth,
17
controlling in addition for the exchange rate regime, and allowing full heterogeneity in
the estimation of country impulse responses.
When we allow for the possibility of asymmetric effects (from negative and
positive shocks), we find that higher trade openness amplifies negative shocks while
improvements in labor market flexibility reduces their impact. We find no significant
effects of the structural determinants on the impact of positive shocks. However, this
may be due to difficulties in the precise estimation of asymmetric affects associated with
wide data variations during transitions between positive and negative shocks.
The role of domestic financial depth deserves special examination. In our basic
exercises, we fail to find a significant effect of financial depth on the output impact of
terms-of-trade shocks. However, this does not mean that financial development is
inconsequential for the issue of vulnerability. On the contrary, further exploration reveals
a significant though more nuanced role for this structural characteristic. In particular,
when we allow for interactions between financial depth and the other characteristics, we
find that higher financial depth helps output stabilization when trade and financial
openness are high and firm entry is unrestricted. This pattern of complementarity is not,
however, present in the relationship between financial depth and labor market flexibility.
In this case, financial depth has a stabilizing effect that compensates for the rigidity of
labor markets.
18
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21
Table 1. Unit Root Tests
ADF by country ADF by country Levin-Lin-Chu
(% cannot reject (% cannot reject
Variable P-value
UR) UR)
(1) (2) (3)
logy 71 84 1.00
logtt 60 83 0.98
Notes:
a) Column (1) reports the percentage of the 90 countries in the sample where the Augmented
Dickey-Fuller test cannot reject the null hypothesis of a unit root when the number of lags
augmenting the test is country specific, as determined by performing the Hall (1990)
procedure on a country-by-country basis.
b) Column (2) reports the same percentage for the case where for all countries the model is
augmented using the median number of lags across countries (2 lags).
c) Column (3) shows the p-value of the Levin-Lin-Chu (2002) test for panel unit roots for the
case where the panel is augmented by 2 lags.
22
Table 2. Sample of Countries and Summary Statistics
Average Standard Standard
Average Labor
terms of deviation deviation Trade Financial Financial Ease of Firm
Country Name output Market
trade growth output terms-of- Openness Depth Openness Entry
growth (%) Flexibility
(%) growth trade growth
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Algeria 0.46 -0.43 2.87 23.14 3.81 -1.12 -1.41 0.54 0.66
Angola -2.26 -3.66 9.28 18.08 4.16 -4.31 -1.55 0.22 0.22
Argentina 0.27 -0.01 5.78 8.17 2.70 -1.78 -0.13 0.34 0.69
Australia 1.86 -0.66 1.92 5.06 3.28 -0.77 1.32 0.64 0.89
Austria 2.20 -0.31 1.56 1.34 3.97 -0.25 1.68 0.70 0.74
Bangladesh 1.62 0.85 2.27 12.44 2.91 -1.60 -1.40 0.50 0.62
Belgium 1.95 -0.12 1.66 1.59 4.83 -0.96 1.56 0.52 0.80
Benin 0.55 -1.69 3.63 14.17 3.71 -2.37 -0.24 0.48 0.69
Bolivia -0.11 -2.19 3.00 11.32 3.73 -1.40 0.68 0.34 0.52
Botswana 5.26 -1.52 3.57 11.19 4.63 -2.06 -0.21 0.65 0.62
Brazil 1.21 -1.73 3.68 9.83 2.75 -1.30 -1.64 0.22 0.45
Burkina Faso 1.19 0.77 3.43 12.52 3.17 -2.01 -0.36 0.47 0.45
Burundi -0.61 -2.78 5.11 33.79 3.28 -2.48 -1.09 0.38 0.25
Cameroon 0.61 0.00 7.03 22.39 3.45 -1.63 -0.47 0.56 0.59
Canada 1.76 0.18 2.28 3.05 3.90 -0.37 2.68 0.66 0.94
Central African Republic -1.42 -1.27 4.61 16.26 3.22 -2.60 -0.66 0.38 0.25
Chad -0.56 -2.94 9.06 13.46 3.26 -2.57 -0.76 0.34 0.41
Chile 3.18 -2.54 5.75 14.51 3.73 -0.85 -1.25 0.50 0.78
China 7.35 -0.93 3.44 5.74 3.16 -0.13 -1.24 0.53 0.61
Colombia 1.34 0.54 2.30 10.19 3.21 -1.33 -1.53 0.41 0.65
Congo, Dem. Rep. -5.17 -0.57 5.19 18.07 3.54 -5.74 -1.09 0.40 0.31
Congo, Rep. 0.37 -0.79 7.02 22.26 4.27 -2.28 -0.91 0.40 0.58
Costa Rica 1.29 0.14 3.73 9.45 4.12 -1.69 -0.56 0.37 0.64
Cote d'Ivoire -1.14 -1.95 4.94 16.36 4.05 -1.16 -0.53 0.47 0.59
Denmark 1.65 0.40 1.93 2.43 3.97 -0.89 1.13 0.75 0.91
Dominican Republic 2.27 -2.49 3.31 11.72 4.02 -1.37 -1.46 0.51 0.60
Ecuador 0.40 -1.73 3.18 13.45 3.71 -1.53 0.04 0.45 0.51
Egypt, Arab Rep. 3.55 -2.80 2.86 11.33 3.57 -1.24 -1.05 0.41 0.59
El Salvador 0.01 0.07 4.83 17.84 3.90 -2.71 -0.64 0.31 0.59
Ethiopia -0.09 0.29 7.67 19.72 3.02 -1.82 -1.14 0.49 0.69
Finland 2.13 -0.08 3.05 3.09 3.89 -0.55 1.54 0.45 0.85
Ghana -0.60 -2.01 5.06 15.93 3.89 -3.26 -1.39 0.65 0.55
Greece 1.42 0.08 2.46 3.41 3.47 -0.99 -0.54 0.33 0.63
Guatemala 0.48 0.01 2.59 8.65 3.52 -1.90 0.63 0.35 0.56
Guinea 1.38 -3.96 1.42 8.91 3.71 -3.18 -1.07 0.40 0.56
Haiti -1.59 1.12 4.82 17.40 3.34 -2.20 0.44 0.40 0.32
Honduras 0.53 0.57 3.25 9.66 4.07 -1.23 0.17 0.44 0.56
Hong Kong, China 4.56 0.38 4.50 1.75 5.22 0.39 2.68 0.73 0.94
Hungary 1.69 -0.88 3.91 3.18 4.36 -1.25 -0.68 0.46 0.76
India 3.12 -0.27 2.92 9.86 2.57 -1.45 -1.03 0.49 0.56
Indonesia 3.87 1.46 4.46 10.94 3.72 -1.20 2.05 0.43 0.45
Iran, Islamic Rep. -0.64 -1.18 7.73 24.31 3.31 -1.25 -0.90 0.48 0.63
Ireland 4.35 -0.46 3.15 2.55 4.57 -0.56 0.58 0.51 0.88
Israel 1.86 0.29 1.96 3.17 4.09 -0.65 -0.39 0.62 0.83
Jamaica -0.21 0.45 4.19 11.39 4.17 -1.33 -0.36 0.66 0.76
Jordan 1.73 1.45 7.52 4.82 4.31 -0.49 -0.18 0.40 0.69
Kenya 0.23 -0.44 2.33 10.48 3.80 -1.24 -0.74 0.66 0.60
Korea, Rep. 5.82 -0.73 3.79 5.29 4.03 -0.30 -0.63 0.49 0.70
Lesotho 2.85 -0.98 6.64 15.82 4.79 -2.01 -0.54 0.55 0.59
Madagascar -1.57 -1.90 3.67 7.81 3.32 -1.86 -0.92 0.39 0.65
Malawi 0.56 -2.13 5.34 10.94 3.97 -2.25 -1.03 0.48 0.63
Malaysia 3.92 -0.14 4.08 6.99 4.71 -0.29 1.63 0.75 0.77
Mali 0.65 0.01 5.93 8.07 3.64 -2.03 -0.24 0.46 0.62
Mauritania 0.10 0.46 3.36 9.42 4.29 -1.16 -1.08 0.41 0.55
23
Table 2. Sample of Countries and Summary Statistics (continued)
Average Standard Standard
Average Labor
terms of deviation deviation Trade Financial Financial Ease of Firm
Country Name output Market
trade growth output terms-of- Openness Depth Openness Entry
growth (%) Flexibility
(%) growth trade growth
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Mexico 1.50 -0.38 3.74 9.90 3.28 -1.67 0.92 0.23 0.66
Morocco 1.57 1.12 4.98 9.09 3.74 -1.25 -1.26 0.49 0.82
Mozambique 0.88 -4.45 7.90 11.96 3.45 -2.19 -1.32 0.26 0.40
Namibia -0.47 -1.94 2.72 11.23 4.58 -0.97 -1.18 0.57 0.64
Netherlands 1.83 -0.14 1.50 1.00 4.52 0.08 2.53 0.46 0.80
New Zealand 0.68 0.37 2.39 5.05 3.79 -0.64 1.70 0.68 0.93
Nicaragua -2.91 -0.76 7.85 25.54 4.01 -1.28 0.11 0.39 0.62
Niger -1.71 0.10 6.05 17.16 3.47 -2.13 -0.53 0.41 0.57
Nigeria -0.96 -0.03 5.59 27.87 4.11 -2.11 -1.19 0.57 0.62
Norway 3.04 -0.48 1.76 7.94 3.95 -0.22 0.54 0.59 0.82
Pakistan 2.47 -2.96 1.93 15.40 3.39 -1.48 -1.09 0.42 0.65
Panama 1.12 -0.21 4.92 3.06 3.67 -0.59 2.68 0.21 0.78
Papua New Guinea 0.07 -1.47 5.43 12.24 4.36 -1.67 -0.23 0.74 0.67
Paraguay 1.32 0.83 4.12 24.12 3.36 -1.73 -0.70 0.27 0.50
Peru -0.42 -1.56 6.10 12.96 3.23 -1.97 0.12 0.27 0.59
Philippines 0.72 0.18 3.76 7.46 3.86 -1.13 -0.57 0.40 0.63
Portugal 2.47 0.02 3.23 4.65 3.87 -0.32 0.09 0.21 0.65
Romania -0.63 -3.03 5.32 29.82 3.85 -2.53 -1.32 0.46 0.73
Rwanda 0.32 1.98 10.06 30.39 3.08 -2.67 -1.00 0.40 0.55
Senegal 0.19 -0.85 4.34 6.69 3.90 -1.29 -0.24 0.46 0.62
Sierra Leone -3.35 1.26 7.15 13.25 3.52 -3.12 -0.85 0.33 0.46
Singapore 5.32 0.06 2.56 1.46 5.67 -0.17 2.00 0.80 0.92
South Africa -0.38 -0.79 2.30 5.61 3.75 -0.69 -1.12 0.64 0.77
Spain 1.98 0.52 1.76 5.11 3.36 -0.24 0.36 0.30 0.68
Sri Lanka 3.48 1.15 1.21 14.27 4.02 -1.73 -0.52 0.58 0.74
Sweden 1.62 -0.47 2.00 2.82 3.94 0.01 1.58 0.58 0.84
Switzerland 0.80 1.25 2.38 3.86 3.99 0.31 2.68 0.64 0.79
Syrian Arab Republic 1.57 -3.00 6.00 13.70 3.80 -2.68 -1.64 0.55 0.65
Thailand 4.66 -1.99 4.37 5.57 3.99 -0.51 -0.04 0.39 0.75
Togo -0.49 -2.48 7.08 23.49 4.05 -1.51 -0.87 0.43 0.44
Tunisia 2.44 -1.18 2.65 4.71 4.14 -0.53 -0.92 0.43 0.78
Turkey 1.93 0.32 4.11 9.72 3.11 -1.86 -0.95 0.45 0.77
Uganda 2.05 -0.91 3.44 20.64 3.11 -3.69 -0.47 0.58 0.60
Uruguay 1.59 -0.26 4.89 6.40 3.35 -1.29 0.87 0.61 0.68
Venezuela -0.94 2.30 4.53 22.09 3.71 -1.07 0.64 0.25 0.55
Zambia -2.23 -5.99 3.98 26.15 4.08 -2.78 -0.71 0.54 0.71
Notes:
a) The different columns of the table show various summary statistics for each of the countries included in the sample, which are
displayed in the corresponding rows. The first column reports the average growth of real GDP per capita during the period 1974-
2000 (or the sub-period for which there was available data).
b) Average terms of trade growth (%): the average growth of the terms of trade index over the same period
Standard deviation output growth: the standard deviation of the growth rates of real GDP per capita over the period 1974-2000
Standard deviation terms-of-trade growth: the standard deviation of terms of trade growth over the period 1974-2000
Trade Openness: Log (Exports + Imports) / GDP
Financial Depth: Log (Private credit) / GDP
Financial Openness: Ito and Chinn measure of capital account openness
Labor market flexibility: 0-1 index obtained from de jure labor regulation
Ease of Firm Entry: 0-1 index obtained combining information on number of procedures, monetary cost, and time to open a new firm.
24
Table 3. Correlations
Cross-sectional correlations between the different variables reported in Table 2.
Terms of Std. dev. Std. dev. Labor
Output Trade Financial Financial Ease of
trade output terms-of- Market
growth Openness Depth Openness Firm Entry
growth growth trade gth Flexibility
Output growth 1.00
Terms of trade growth 0.11 1.00
Std. dev. output growth -0.40 -0.13 1.00
Std. dev. terms-of-trade gth -0.51 -0.23 0.56 1.00
Trade Openness 0.25 -0.02 -0.13 -0.26 1.00
Financial Depth 0.59 0.30 -0.47 -0.61 0.31 1.00
Financial Openness 0.25 0.36 -0.33 -0.52 0.35 0.54 1.00
Labor Market Flexibility 0.30 0.06 -0.31 -0.23 0.46 0.29 0.27 1.00
Ease of Firm Entry 0.47 0.21 -0.45 -0.57 0.41 0.66 0.49 0.55 1.00
25
Table 4. Basic Results under Symmetric Analysis
Cumulative output impact of a 1-std.deviation terms-of-trade shock for low and high values of 5 structural characteristics
Labor Market
Trade Openness Financial Depth Financial Openness Ease of Firm Entry
Flexibility
Low 0.012 0.903 1.031 1.193 0.885
(0.224) (0.209) (0.249) (0.243) (0.257)
High 1.439 0.905 0.688 0.450 0.940
(0.263) (0.261) (0.208) (0.262) (0.275)
Difference 1.427 0.002 -0.343 -0.742 0.055
(0.300) (0.267) (0.243) (0.302) (0.351)
Test Ho:Diff.=0 (one-tail) ** - * ** -
Notes:
a) The reported impacts are given in percentage points of GDP.
b) Trade Openness: Log (Exports + Imports) / GDP
Financial Depth: Log (Private credit) / GDP
Financial Openness: Ito and Chinn measure of capital account openness
Labor market flexibility: 0-1 index obtained from de jure labor regulation
Ease of Firm Entry: 0-1 index obtained combining information on number of procedures, monetary cost, and time to open a new firm.
c) "Low" and "High" correspond to the 25th and 75th percentiles of the world distribution of the respective structural characteristic.
d) Numbers in parentheses are standard errors of corresponding cumulative output impact.
e) (*) and (**) indicate 10% and 5% significance, respectively. Critical values are obtained from empirical distribution (which may have
non-Gaussian properties).
26
Table 5. Robustness
Cumulative output impact of a 1-std.deviation terms-of-trade shock for low and high values of 5 structural characteristics
Labor Market Exchange Rate
Trade Openness Financial Depth Financial Openness Ease of Firm Entry
Flexibility Regime
Low 0.012 0.903 1.031 1.193 0.885
Benchmark High 1.439 0.905 0.688 0.450 0.940
Diff. 1.427 0.002 -0.343 -0.742 0.055
3 lags in common Low -0.169 0.686 0.647 0.999 0.407
lag structure High 1.021 0.501 0.452 -0.092 0.887
Diff. 1.190 -0.185 -0.195 -1.091 0.481
Only developing Low 0.020 0.931 1.063 1.226 0.915
countries High 1.485 0.938 0.718 0.479 0.974
Diff. 1.464 0.007 -0.345 -0.747 0.059
Initial financial Low 0.189 1.128 1.262 1.427 0.961
depth (instead of High 1.649 1.084 0.828 0.590 1.363
average) Diff. 1.460 -0.044 -0.434 -0.837 0.402
Including Low -0.002 0.791 0.964 1.111 0.808 0.843
exchange rate High 1.350 0.878 0.637 0.422 0.909 0.970
regime Diff. 1.352 0.087 -0.327 -0.689 0.101 0.127
Notes:
a) The reported impacts are given in percentage points of GDP.
b) Trade Openness: Log (Exports + Imports) / GDP
Financial Depth: Log (Private credit) / GDP
Financial Openness: Ito and Chinn measure of capital account openness
Labor market flexibility: 0-1 index obtained from de jure labor regulation
Ease of Firm Entry: 0-1 index obtained combining information on number of procedures, monetary cost, and time to open a new firm.
c) "Low" and "High" correspond to the 25th and 75th percentiles of the world distribution of the respective structural characteristic.
d) The benchmark includes all countries and sets the common lag structure to 2 lags.
e) For exchange regime, low and high mean flexible and pegged regime, respectively.
27
Table 6. Shock Impact and Structural Characteristics
Dependent variable: Cumulative GDP impact of a 1-std.deviation terms-of-trade shock
OLS
Constant -0.2548
(0.2637)
Trade Openness 0.1443 **
(0.0590)
Financial Depth 0.0311
(0.0380)
Financial Openness -0.0412
(0.0292)
Labor Market Flexibility -0.5369 **
(0.2512)
Ease of Firm Entry 0.1635
(0.2770)
R-square 0.09
Number of countries 100
Notes:
a) Trade Openness: Log (Exports + Imports) / GDP
Financial Depth: Log (Private credit) / GDP
Financial Openness: Ito and Chinn measure of capital account openness
Labor market flexibility: 0-1 index obtained from de jure labor regulation
Ease of Firm Entry: 0-1 index obtained combining information on number of procedures,
monetary cost, and time to open a new firm.
b) Robust standard erroes in parentheses below corresponding coefficient.
c) Regression is estimated using a robust procedure that reduces the influence of outliers.
d) * : Significant at 10%
** : Significant at 5 %
28
Table 7. Asymmetric Effects
Cumulative output impact of 1-std.deviation terms-of-trade negative and positive shocks for low and high values of 5 structural
characteristics
Labor Market
Trade Openness Financial Depth Financial Openness Ease of Firm Entry
Flexibility
Negative Positive Negative Positive Negative Positive Negative Positive Negative Positive
Low 0.115 0.400 -1.193 0.666 -1.294 0.855 -1.513 0.903 -1.216 0.511
(0.445) (0.456) (0.410) (0.382) (0.471) (0.478) (0.457) (0.452) (0.487) (0.511)
High -1.977 0.886 -1.192 0.729 -1.020 0.446 -0.688 0.392 -1.149 1.062
(0.495) (0.492) 0.484) (0.515) (0.402) (0.393) (0.492) (0.512) (0.506) (0.497)
Difference -2.092 0.4858 0.000 0.0633 0.273 -0.4088 0.825 -0.5108 0.067 0.5514
(0.518) (0.512) (0.422) (0.453) (0.387) (0.379) (0.492) (0.498) (0.589) (0.623)
Test Ho:Diff.=0 (one-tail) ** - - - - - ** - - -
Notes:
a) The reported impacts are given in percentage points of GDP.
b) Trade Openness: Log (Exports + Imports) / GDP
Financial Depth: Log (Private credit) / GDP
Financial Openness: Ito and Chinn measure of capital account openness
Labor market flexibility: 0-1 index obtained from de jure labor regulation
Ease of Firm Entry: 0-1 index obtained combining information on number of procedures, monetary cost, and time to open a new firm.
c) "Low" and "High" correspond to the 25th and 75th percentiles of the world distribution of the respective structural characteristic.
d) Numbers in parentheses are standard errors of corresponding cummulative output impact.
e) (*) and (**) indicate 10% and 5% significance, respectively. Critical values are obtained from empirical distribution (which may have
non-Gaussian properties).
29
Table 8. Interactions between Financial Depth and Other Structural Characteristics
Cumulative output impact of a 1-std.deviation terms-of-trade shock for low and high values of 5 structural characteristics
Trade Openness Financial Openness Labor Market Flexibility Ease of Firm Entry
Negative Positive Negative Positive Negative Positive Negative Positive
Low 0.316 1.779 0.914 1.770 1.566 0.703 1.089 1.494
(0.263) (0.270) (0.279) (0.321) (0.314) (0.293) (0.255) (0.341)
High 0.876 1.240 1.291 0.168 0.948 0.762 0.499 0.000
(0.338) (0.336) (0.377) (0.253) (0.307) (0.336) (0.390) (0.000)
Difference 0.559 -0.539 0.378 -1.602 -0.618 0.059 -0.590 -1.494
(0.342) (0.353) (0.342) (0.362) (0.345) (0.332) (0.289) (0.390)
Test Ho:Diff.=0 (one-tail) ** * - ** ** - ** **
Notes:
a) The reported impacts are given in percentage points of GDP.
b) Trade Openness: Log (Exports + Imports) / GDP
Financial Depth: Log (Private credit) / GDP
Financial Openness: Ito and Chinn measure of capital account openness
Labor market flexibility: 0-1 index obtained from de jure labor regulation
Ease of Firm Entry: 0-1 index obtained combining information on number of procedures, monetary cost, and time to open a new firm.
c) "Low" and "High" correspond to the 25th and 75th percentiles of the world distribution of the respective structural characteristics.
d) (*) and (**) indicate 10% and 5% significance, respectively. Critical values are obtained from empirical distribution (which may have
non-Gaussian properties).
30
Figure 1. Cumulative Output Impact of Terms-of-Trade Shock ­ Symmetric Case
Low Trade Openness High Trade Openness
e)tats 0.020 e)tats 0.020
0.015 0.015
eadyts 0.010 eadyts 0.010
omrf omrf
snoitaiv 0.005 0.005
de 0.000 snoitaiv
de 0.000
ogl( -0.005 ogl( -0.005
putt putt
Ou -0.010 Ou -0.010
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Financial Depth High Financial Depth
e)tats 0.020 e)tats 0.020
0.015 0.015
eadyts 0.010 eadyts 0.010
omrf omrf
snoitaiv 0.005 0.005
de 0.000 snoitaiv
de 0.000
ogl( -0.005 ogl( -0.005
putt putt
Ou -0.010 Ou -0.010
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Financial Openness High Financial Openness
e)tats 0.020 e)tats 0.020
0.015 0.015
eadyts 0.010 eadyts 0.010
omrf omrf
snoitaiv 0.005 0.005
de 0.000 snoitaiv
de 0.000
ogl( -0.005 ogl( -0.005
putt putt
Ou -0.010 Ou -0.010
1 6 11 16 21 1 6 11 16 21
Time (years) Time (years)
Low Labor Market Flexibility High Labor Market Flexibility
e) 0.020 0.020
atts e)tats
0.015 0.015
adyets 0.010 eadyts 0.010
omrf omrf
0.005 0.005
onsi
ati
0.000
dev 0.000 snoitaiv
de
ogl( -0.005 ogl( -0.005
put putt
Out -0.010 Ou -0.010
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Ease of Firm Entry High Ease of Firm Entry
e) 0.020 e) 0.020
atts atts
0.015 0.015
adyets 0.010 adyets 0.010
omrf omrf
0.005 0.005
onsi onsi
ati ati
dev 0.000 dev 0.000
ogl( -0.005 ogl( -0.005
put put
Out -0.010 Out -0.010
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Notes:
a) See Table 1 for variable definitions.
b) Bands are 90% confidence intervals.
31
Figure 2. Cumulative Output Impact of Negative Terms-of-Trade Shock
Low Trade Openness High Trade Openness
e) 0.010 0.010
atts 0.005 e)tats 0.005
adyets 0.000 eadyts 0.000
morf -0.005 omrf -0.005
s -0.010 s -0.010
oniati -0.015 onitaiv -0.015
dev de
gol( -0.020 ogl( -0.020
putt -0.025 putt -0.025
Ou -0.030 Ou -0.030
1 6 11 16 21 -5 0 5 10 15
Time (years) Time (years)
Low Financial Depth High Financial Depth
e) 0.005 e) 0.005
atts atts
0.000 0.000
eadyts -0.005 eadyts -0.005
omrf omrf
-0.010 -0.010
onsi onsi
ati ati
dev -0.015 dev -0.015
ogl( -0.020 ogl( -0.020
put put
Out -0.025 Out -0.025
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Financial Openness High Financial Openness
e) 0.005 e) 0.005
atts atts
0.000 0.000
eadyts -0.005 eadyts -0.005
omrf omrf
-0.010 -0.010
onsi onsi
ati ati
dev -0.015 dev -0.015
ogl( -0.020 ogl( -0.020
put put
Out -0.025 Out -0.025
-5 0 5 10 15 1 6 11 16 21
Time (years) Time (years)
Low Labor Market Flexibility High Labor Market Flexibility
e) 0.005 0.005
atts e)tats
0.000 0.000
adyets -0.005 eadyts -0.005
omrf omrf
-0.010 s -0.010
onsi
ati
-0.015
dev -0.015 onitaiv
de
ogl( -0.020 ogl( -0.020
put putt
Out -0.025 Ou -0.025
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Ease of Firm Entry High Ease of Firm Entry
e) 0.005 e) 0.005
atts atts
0.000 0.000
adyets -0.005 adyets -0.005
omrf omrf
-0.010 -0.010
onsi onsi
ati ati
dev -0.015 dev -0.015
ogl( -0.020 ogl( -0.020
put put
Out -0.025 Out -0.025
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Notes:
a) See Table 1 for variable definitions.
b) Bands are 90% confidence intervals.
32
Figure 3. Cumulative Output Impact of Positive Terms-of-Trade Shock
Low Trade Openness High Trade Openness
e)tats 0.025 0.025
0.020 e)tats 0.020
eadyts 0.015 eadyts 0.015
omrf 0.010 omrf 0.010
s s
onitaiv 0.005 onitaiv 0.005
de 0.000 de 0.000
ogl( ogl(
-0.005 -0.005
putt putt
Ou -0.010 Ou -0.010
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Financial Depth High Financial Depth
e)tats 0.020 e)tats 0.020
0.015 0.015
eadyts eadyts
omrf 0.010 omrf 0.010
s s
onitaiv 0.005 onitaiv 0.005
de de
ogl( 0.000 ogl( 0.000
putt putt
Ou -0.005 Ou -0.005
-5 0 5 10 15 -5 0 5 10 15
Time (years) Time (years)
Low Financial Openness High Financial Openness
e)tats 0.020 e)tats 0.020
0.015 0.015
eadyts eadyts
omrf 0.010 omrf 0.010
s s
onitaiv 0.005 onitaiv 0.005
de de
ogl( 0.000 ogl( 0.000
putt putt
Ou -0.005 Ou -0.005
1 6 11 16 21 1 6 11 16 21
Time (years) Time (years)
Low Labor Market Flexibility High Labor Market Flexibility
e) 0.020 0.020
atts e)tats
0.015 0.015
adyets 0.010 eadyts 0.010
omrf omrf
0.005 s 0.005
onsi
ati
0.000
dev 0.000 onitaiv
de
ogl( -0.005 ogl( -0.005
put putt
Out -0.010 Ou -0.010
-5 0 5 10 15 1 6 11 16 21
Time (years) Time (years)
Low Ease of Firm Entry High Ease of Firm Entry
e) 0.020 e) 0.020
atts atts
0.015 0.015
adyets 0.010 adyets 0.010
omrf omrf
0.005 0.005
onsi onsi
ati ati
dev 0.000 dev 0.000
ogl( -0.005 ogl( -0.005
put put
Out-0.010 Out-0.010
1 6 11 16 21 1 6 11 16 21
Time (years) Time (years)
Notes:
a) See Table 1 for variable definitions.
b) Bands are 90% confidence intervals.
33